Entry requirements

120 credits, of which Computer programming I and Scientific computing II or the equivalent is included.

Learning outcomes

To pass, the student should be able to

• describe the data flow in a visualisation system;
• outline the methods that transform the data and information to visual representations;
• use and program advanced software for various visualisation techniques.

Content

Scientific Visualisation is an area concerned with the visualisation of large and complex data sets, where the data might come from experiments or computations. Visualisation is a way, in many cases the only possible way, to achieve insight and knowledge.

Discrete models. Volume rendering: ray-tracing, splatting, texture based. Isosurface reconstruction. Transformation of discrete volume data to polygonal representations. Mesh topologies and mesh simplification. Visualisation techniques. Visual aspects based on perception. Particle rendering. Algorithms for programmable graphics hardware. Applied visualisation. The course includes projects such as programming in VTK (the Visualisation Toolkit).

Instruction

Lectures, laboratory work and compulsory assignments.

Assessment

Written examination at the end of the course. Passed laboratory course and approved compulsory assignments are also required.